OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration

• URL: http://arxiv.org/abs/2607.01531v1
• Date: Wed, 01 Jul 2026 23:04:47 GMT
• Title: OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration
• Abstract summary: We introduce OPINE-World, an agent that learns an object-centric programmatic world model online from interaction.<n>OPINE-World solves 20 of 25 games without per-game training and reaches an action-efficiency score of 78.4 against the human baseline.
• Score: 29.764166104307932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks. World models learned with deep networks are flexible but data-hungry and transfer poorly beyond their training distribution. Program-synthesized world models, written as source code by LLMs and refined through counterexample-guided inductive synthesis (CEGIS), are instead data-efficient and reusable, yet they have been demonstrated mainly on structured-state worlds with a given object vocabulary, and a single program search does not scale to pixel-rendered environments whose object structure must be hypothesized flexibly. We introduce OPINE-World, an LLM agent that learns an object-centric programmatic world model online from interaction. OPINE-World couples two cooperating agents in a loop of hypothesis and test, one acting in the environment and one synthesizing the model in code with replay verification and model-based planning, and it steers exploration with a Bayesian measure of object-type adequacy we call ontology error. We evaluate OPINE-World on ARC-AGI-3, a benchmark for skill-acquisition efficiency in which the object vocabulary, the goal, and the action semantics are withheld. OPINE-World solves 20 of 25 games without per-game training and reaches an action-efficiency score of 78.4 against the human baseline.

Related papers

• Bridging the Agent-World Gap: Text World Models for LLM-based Agents [83.8237017249529]
  Large language model (LLM)-based agents are increasingly used in interactive textual environments.<n>Many remain largely reactive, mapping observations to actions without an explicit model of how these environments are structured and evolve.<n>This motivates text world models (TWMs): transition models over textual states that, given a state and a candidate action, predict the resulting webpage, terminal output, API response, or user reply.<n>We systematically review text world models for LLM-based agents, organized around a formal framework and the agent lifecycle.
  arXiv  Detail & Related papers  (2026-06-08T04:58:52Z)
• Learning POMDP World Models from Observations with Language-Model Priors [52.11151769796862]
  Partially-observable Markov decision processes (POMDPs) provide a flexible modeling class for internal world models.<n>We ask whether language-model priors can reduce costly interaction by leveraging prior knowledge.<n>We show that emphPinductor matches the performance and sample efficiency of LLM-based POMDP learning methods.
  arXiv  Detail & Related papers  (2026-05-13T16:18:15Z)
• From Word to World: Can Large Language Models be Implicit Text-based World Models? [82.47317196099907]
  Agentic reinforcement learning increasingly relies on experience-driven scaling.<n>World models offer a potential way to improve learning efficiency through simulated experience.<n>We study whether large language models can reliably serve this role and under what conditions they meaningfully benefit agents.
  arXiv  Detail & Related papers  (2025-12-21T17:28:42Z)
• WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World [100.68103378427567]
  Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally.<n>We introduce WorldLens, a full-spectrum benchmark evaluating how well a model builds, understands, and behaves within its generated world.<n>We further construct WorldLens-26K, a large-scale dataset of human-annotated videos with numerical scores and textual rationales, and develop WorldLens-Agent.
  arXiv  Detail & Related papers  (2025-12-11T18:59:58Z)
• Object-Centric World Models for Causality-Aware Reinforcement Learning [13.063093054280946]
  We propose emph Transformer Imagination with CAusality-aware reinforcement learning (ASTICA)<n>A unified framework in which object-centric Transformers serve as the world model and causality-aware policy and value networks.<n>Experiments on object-rich benchmarks demonstrate that STICA consistently outperforms state-of-the-art agents in both sample efficiency and final performance.
  arXiv  Detail & Related papers  (2025-11-18T08:53:09Z)
• Learning Interactive World Model for Object-Centric Reinforcement Learning [27.710001478315288]
  We introduce a unified framework that learns structured representations of both objects and their interactions within a world model.<n>FIOC-WM captures environment dynamics with disentangled and modular representations of object interactions.<n>On simulated robotic and embodied-AI benchmarks, FIOC-WM improves policy-learning sample efficiency and generalization over world-model baselines.
  arXiv  Detail & Related papers  (2025-11-04T03:35:58Z)
• World Model Implanting for Test-time Adaptation of Embodied Agents [29.514831254621438]
  In embodied AI, a persistent challenge is enabling agents to robustly adapt to novel domains without requiring extensive data collection or retraining.<n>We present a world model implanting framework (WorMI) that combines the reasoning capabilities of large language models with independently learned, domain-specific world models.<n>We evaluate our WorMI on the VirtualHome and ALFWorld benchmarks, demonstrating superior zero-shot and few-shot performance compared to several LLM-based approaches.
  arXiv  Detail & Related papers  (2025-09-04T07:32:16Z)
• Relational Object-Centric Actor-Critic [44.99833362998488]
  Recent works highlight that disentangled object representations can aid policy learning in image-based, object-centric reinforcement learning tasks.<n>This paper proposes a novel object-centric reinforcement learning algorithm that integrates actor-critic and model-based approaches.<n>We evaluate our method in a simulated 3D robotic environment and a 2D environment with compositional structure.
  arXiv  Detail & Related papers  (2023-10-26T06:05:12Z)
• Transferring Foundation Models for Generalizable Robotic Manipulation [82.12754319808197]
  We propose a novel paradigm that effectively leverages language-reasoning segmentation mask generated by internet-scale foundation models.<n>Our approach can effectively and robustly perceive object pose and enable sample-efficient generalization learning.<n>Demos can be found in our submitted video, and more comprehensive ones can be found in link1 or link2.
  arXiv  Detail & Related papers  (2023-06-09T07:22:12Z)
• Bridging the Gap to Real-World Object-Centric Learning [66.55867830853803]
  We show that reconstructing features from models trained in a self-supervised manner is a sufficient training signal for object-centric representations to arise in a fully unsupervised way. Our approach, DINOSAUR, significantly out-performs existing object-centric learning models on simulated data.
  arXiv  Detail & Related papers  (2022-09-29T15:24:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.